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Learning to predict large-scale coral bleaching from past events: A Bayesian approach using remotely sensed data, in-situ data, and environmental proxies

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Abstract

Ocean warming and coral bleaching are patchy phenomena over a wide range of scales. This paper is part of a larger study that aims to understand the relationship between heat stress and ecological impact caused by the 2002-bleaching event in the Great Barrier Reef (GBR). We used a Bayesian belief network (BBN) as a framework to refine our prior beliefs and investigate dependencies among a series of proxies that attempt to characterize potential drivers and responses: the remotely sensed environmental stress (sea surface temperature — SST); the geographic setting; and topographic and ecological attributes of reef sites for which we had field data on bleaching impact. Sensitivity analyses helped us to refine and update our beliefs in a manner that improved our capacity to hindcast areas of high and low bleaching impact. Our best predictive capacity came by combining proxies for a site’s heat stress in 2002 (remotely sensed), acclimatization temperatures (remote sensed), the ease with which it could be cooled by tidal mixing (modeled), and type of coral community present at a sample of survey sites (field data). The potential for the outlined methodology to deliver a transparent decision support tool to aid in the process of identifying a series of locations whose inclusion in a network of protected areas would help to spread the risk of bleaching is discussed.

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Acknowledgements

We thank the following colleagues who have contributed to this work: Emre Turak and Mary Wakeford for assisting Terry Done with the bleaching assessments; Glenn De’ath and Ray Berkelmans for allowing us access to max3day prior to publication; Glenn De’ath for assistance in defining coral communities; Stuart Kininmonth and Steve Edgar for development of the GIS; Mike Mahoney for providing the SST data to enable the development of various SST indices in the GIS; Craig Steinberg for unpublished modeled current data that allowed us to develop the proxy cost100. The manuscript also benefited from the comments of two anonymous reviewers.

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Authors and Affiliations

  1. Australian Institute of Marine Science, Townsville, Q4810, Australia

    Scott Wooldridge & Terry Done

  2. Cooperative Research Centre for the Great Barrier Reef World Heritage Area, Townsville, Q4810, Australia

    Scott Wooldridge & Terry Done

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  1. Scott Wooldridge
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  2. Terry Done
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Correspondence to Scott Wooldridge.

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Wooldridge, S., Done, T. Learning to predict large-scale coral bleaching from past events: A Bayesian approach using remotely sensed data, in-situ data, and environmental proxies. Coral Reefs 23, 96–108 (2004). https://doi.org/10.1007/s00338-003-0361-y

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  • DOI: https://doi.org/10.1007/s00338-003-0361-y

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